Quantifying Trading Behavior in Financial Markets Using Google Trends

Preis, Tobias; Moat, Helen Susannah; Stanley, H. Eugene

doi:10.1038/srep01684

Cited by 780 publications

(556 citation statements)

References 38 publications

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“…In this article, we fixed the search query terms after 2010 so as to directly compare our results with GFT, which has kept the same query terms since 2010; future application of ARGO may update search terms more frequently. ARGO can be easily generalized to any temporal and spatial scales for a variety of diseases or social events amenable to be tracked by Internet searches or services (3,4,8,9,29,30,38,39). Further improvements in influenza prediction may come from combining multiple predictors constructed from disparate data sources (40).…”

Section: Strength Of Argomentioning

confidence: 99%

Accurate estimation of influenza epidemics using Google search data via ARGO

Yang

Santillana

Kou

2015

Proc. Natl. Acad. Sci. U.S.A.

363

443

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Section: Strength Of Argomentioning

confidence: 99%

Accurate estimation of influenza epidemics using Google search data via ARGO

Yang

Santillana

Kou

2015

Proc. Natl. Acad. Sci. U.S.A.

363

443

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“…Due to the decentralized structure of P2P ecosystems, it is very difficult to gather large-scale data about interactions and behavioral patterns of the users without their explicit consent; this is in contrast to other forms of online exchange where all of the information is stored in a central system, be it publicly accessible as in Wikipedia (7), partially accessible through a public interface as in Twitter (8,9) or Google [through its search logs (10) or its public services (11,12)], or restricted as in Facebook (13,14) or in email communications within organizations (15)(16)(17)(18).…”

mentioning

confidence: 99%

Impact of heterogeneity and socioeconomic factors on individual behavior in decentralized sharing ecosystems

Gavaldà-Miralles¹,

Choffnes

Otto

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Tens of millions of individuals around the world use decentralized content distribution systems, a fact of growing social, economic, and technological importance. These sharing systems are poorly understood because, unlike in other technosocial systems, it is difficult to gather large-scale data about user behavior. Here, we investigate user activity patterns and the socioeconomic factors that could explain the behavior. Our analysis reveals that (i) the ecosystem is heterogeneous at several levels: content types are heterogeneous, users specialize in a few content types, and countries are heterogeneous in user profiles; and (ii) there is a strong correlation between socioeconomic indicators of a country and users behavior. Our findings open a research area on the dynamics of decentralized sharing ecosystems and the socioeconomic factors affecting them, and may have implications for the design of algorithms and for policymaking.human activity | Internet | content sharing | privacy | BitTorrent E very month, ∼150 million users worldwide share files over the Internet using BitTorrent (1), the most widely used decentralized peer-to-peer (P2P) communication protocol. Eleven years after its inception, file sharing through BitTorrent is one of the top three major contributors to the overall Internet traffic, accounting for 9-27% of the total traffic, depending on the continent (2, 3).The expansion in scale and breadth of decentralized filesharing has highlighted the conflicts between the interests of creators (musicians and writers, e.g.) and those of P2P users. Creators and creative industries argue that they are being deprived of fair compensation for their work (4), which is being widely distributed for free in violation of copyright laws. Users, however, argue that P2P can be (and is) used for sharing nonproprietary contents, and warn that widespread monitoring of online activity by corporations and law enforcement violates P2P users' right to privacy. Proof of the complexity of the situation includes the rejection of the AntiCounterfeiting Trade Agreement by the European Parliament and the controversy with the Stop Online Piracy Act in the United States.Despite the growing social, economic, and technological importance of BitTorrent (4), there is currently little understanding of how users behave in this complex technosocial (5, 6) ecosystem. Due to the decentralized structure of P2P ecosystems, it is very difficult to gather large-scale data about interactions and behavioral patterns of the users without their explicit consent; this is in contrast to other forms of online exchange where all of the information is stored in a central system, be it publicly accessible as in Wikipedia (7), partially accessible through a public interface as in Twitter (8, 9) or Google [through its search logs (10) or its public services (11, 12)], or restricted as in Facebook (13,14) or in email communications within organizations (15-18).Because of the difficulty to collect complete user-level data of large and representative sample...

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“…The search behavior shows a clear pattern. Similar Google Trends patterns have proven to have significant correlations with a large variety of aspects in commerce, including stock market movements [67], trading behavior [68], automobile sales [69], company evaluations [70], and private consumption [71].…”

Section: Big Data: Informing Business Growth Strategiesmentioning

confidence: 89%

The More You Know, the More You Can Grow: An Information Theoretic Approach to Growth in the Information Age

Hilbert

2017

Entropy

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In our information age, information alone has become a driver of social growth. Information is the fuel of "big data" companies, and the decision-making compass of policy makers. Can we quantify how much information leads to how much social growth potential? Information theory is used to show that information (in bits) is effectively a quantifiable ingredient of growth. The article presents a single equation that allows both to describe hands-off natural selection of evolving populations and to optimize population fitness in uncertain environments through intervention. The setup analyzes the communication channel between the growing population and its uncertain environment. The role of information in population growth can be thought of as the optimization of information flow over this (more or less) noisy channel. Optimized growth implies that the population absorbs all communicated environmental structure during evolutionary updating (measured by their mutual information). This is achieved by endogenously adjusting the population structure to the exogenous environmental pattern (through bet-hedging/portfolio management). The setup can be applied to decompose the growth of any discrete population in stationary, stochastic environments (economic, cultural, or biological). Two empirical examples from the information economy reveal inherent trade-offs among the involved information quantities during growth optimization.

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Quantifying Trading Behavior in Financial Markets Using Google Trends

Cited by 780 publications

References 38 publications

Accurate estimation of influenza epidemics using Google search data via ARGO

Accurate estimation of influenza epidemics using Google search data via ARGO

Impact of heterogeneity and socioeconomic factors on individual behavior in decentralized sharing ecosystems

The More You Know, the More You Can Grow: An Information Theoretic Approach to Growth in the Information Age

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